Pairwise FCM based feature weighting for improved classification of vertebral column disorders

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dc.contributor.authorUnal, Yavuz
dc.contributor.authorPolat, Kemal
dc.contributor.authorKocer, H. Erdinc
dc.date.accessioned2020-03-26T18:51:57Z
dc.date.available2020-03-26T18:51:57Z
dc.date.issued2014
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractIn this paper, an innovative data pre-processing method to improve the classification performance and to determine automatically the vertebral column disorders including disk hernia (DH), spondylolisthesis (SL) and normal (NO) groups has been proposed. In the classification of vertebral column disorders' dataset with three classes, a pairwise fuzzy C-means (FCM) based feature weighting method has been proposed. In this method, first of all, the vertebral column dataset has been grouped as pairwise (DH-SL, DH-NO, and SL-NO) and then these pairwise groups have been weighted using a FCM based feature set. These weighted groups have been classified using classifier algorithms including multilayer perceptron (MLP), k-nearest neighbor (k-NN), Naive Bayes, and support vector machine (SVM). The general classification performance has been obtained by averaging of classification accuracies obtained from pairwise classifier algorithms. To evaluate the performance of the proposed method, the classification accuracy, sensitivity, specificity, ROC curves, and f-measure have been used. Without the proposed feature weighting, the obtained f-measure values were 0.7738 for MLP classifier, 0.7021 for k-NN, 0.7263 for Naive Bayes, and 0.7298 for SVM classifier algorithms in the classification of vertebral column disorders' dataset with three classes. With the pairwise fuzzy C-means based feature weighting method, the obtained f-measure values were 0.9509 for MLP, 0.9313 for k-NN, 0.9603 for Naive Bayes, and 0.9468 for SVM classifier algorithms. The experimental results demonstrated that the proposed pairwise fuzzy C-means based feature weighting method is robust and effective in the classification of vertebral column disorders' dataset. in the future, this method could be used confidently for medical datasets with more classes. (c) 2013 Elsevier Ltd. All rights reserved.en_US
dc.description.sponsorshipScientific Research Projects (BAP) of Amasya UniversityAmasya Universityen_US
dc.description.sponsorshipThis study is supported by the Scientific Research Projects (BAP) of Amasya University.en_US
dc.identifier.doi10.1016/j.compbiomed.2013.12.004en_US
dc.identifier.endpage70en_US
dc.identifier.issn0010-4825en_US
dc.identifier.issn1879-0534en_US
dc.identifier.pmid24529206en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage61en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.compbiomed.2013.12.004
dc.identifier.urihttps://hdl.handle.net/20.500.12395/31054
dc.identifier.volume46en_US
dc.identifier.wosWOS:000332910900007en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherPERGAMON-ELSEVIER SCIENCE LTDen_US
dc.relation.ispartofCOMPUTERS IN BIOLOGY AND MEDICINEen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectVertebral columnen_US
dc.subjectPairwise Fuzzy C-means clustering based feature weightingen_US
dc.subjectClassificationen_US
dc.subjectData pre-processingen_US
dc.titlePairwise FCM based feature weighting for improved classification of vertebral column disordersen_US
dc.typeArticleen_US

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